Description Usage Arguments Details Value Examples

`pipeLIMMA`

Run a pipeline of LIMMA functions for differential gene expression.

1 2 3 4 |

`counts` |
A count matrix |

`info` |
An experimental design matrix |

`formula` |
A character string that can be coerced to a formula. Specify if a contrast model is not desired. |

`contrast.matrix` |
A matrix of contrasts, usually created by a call from limma:makeContrasts. Used only when limma:contrast.fit is intended as the statistical modeling function. NULL values force a traditional test of effects via limma::lmFit/ebayes. |

`block` |
A string that represents an individual that was repeatedly measured, if NULL, runs the analysis without a blocking / duplicate correlation factor |

`design` |
A design matrix, usually created by a call from "model.matrix". Used only if limma:contrast.fit is intended as the statistical modeling function. NULL values force a traditional test of effects via limma::lmFit/ebayes. |

`runVoom` |
Logical, if TRUE, normalizes the counts matrix via voom. If FALSE, assumes the counts matrix is already voom-normalized. Pre-running voom will speed up analyses with multiple pipeLIMMA calls. |

`use.qualityWeights` |
Logical, run voom with quality weights or not? |

`use.topTable` |
Logical, report F-statistics across all factors? If true, a third element is returned called fstats. |

`geneIDs` |
The names of genes. If NA, use row names from counts matrix |

`verbose` |
Logical, return progress updates? |

`plotVoom` |
Logical, plot the voom fit? Defaults to FALSE |

`...` |
additional arguments, not currently in use. |

This function runs the following pipeline:

1. calculate normalization factors via edgeR::calcNormFactors

2. Run limma::voom transformation

3. Run limma:lmFit linear modeling

4. Run limma::ebayes statistical modeling

5. Collect Log2 Fold-Changes using limma:topTable

6. Collate and ouput statistics and voom transformed data

a list with 2 elements (if simple=TRUE)

"stats": the statsistics generated from ebayes and topTable

"voom": the voom normalized counts data

"fstats": if a formula is provided, the toptable returned fstatsistics across all estimates of each factor

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## Not run:
data(kidney) # from simseq
counts<-kidney$counts
counts<-counts[sample(1:nrow(counts),1000),]
info<-data.frame(rep=kidney$replic,
treatment=kidney$treatment)
stats<-pipeLIMMA(counts=counts,
info=info,
formula = " ~ treatment",
block=info$rep)
stats<-pipeLIMMA(counts=counts,
info=info,
formula = " ~ treatment",
block=NULL)
## End(Not run)
``` |

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